How Hudson River Trading Actually Uses AI
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What is the main topic discussed in this episode?
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Bloomberg Audio Studios, Podcasts, Radio News.
Hello and welcome to another episode of the Odd Lots Podcast. I'm Jill Wisenthal.
And I'm Tracy Alloway.
Tracy, I've I've always had this idea for the podcast or a thing that I wanted to do.
Okay.
conceptually with podcasts is schedule every guest for two interviews. So you have the opening interview and you ask a bunch of questions. And then it's oh God, I really wish I had followed up on that. I had more. I was just starting to sort of get my head around this thing. Now I could have asked the good questions. And then like have the person come back next week. Also the the audience complains. I wish you had asked that. And then fill in all those gaps that had been inspired by the previous conversation.
I don't think it's a bad idea. I think it would double the number of episodes that we put out. Yeah. But sure, there are topics that come up, usually things that we're just kind of new to. Yeah. And we're trying to learn about, specifically technical things. And one of those has to be AI, right?
AI. And also, you know, I really had a great time, I guess, last month. We were in Chicago. Yeah. We talked to a bunch of different it was like a trading related Trip, we uh interviewed Don Wilson, we interviewed the head of the CME, we had some other chats, and they're all about the world of trading. When it comes to trading, it's like, you know, we talk to long-term investors, portfolio managers, endowments, we talked to some people in the hedge fund space who like maybe have a holding period of several weeks or whatever. I actually really wanna learn more about the trading, like these people who have like a holding time of one second or something like that. 'Cause that's where a lot of the tech and a lot of the actual like action is.
And how that world makes money and how they actually deploy technology is very interesting, but still something I don't have uh my handle on.
Well, the practical application, right? And also the culture of AI on Wall Street. I find that really interesting because I remember, I guess it was like more than a decade ago, but remember Lloyd Blankfein saying that Goldman Sachs is a technology. Oh, yeah. Yeah, Jacob Morgan. Yeah. Yeah. And all these bank CEOs saying, we're gonna install ping pong tables to get all the coders. And now I see ads at trading firms, and it's like, we have a data center full of B200s, or we have a data center full. G three hundreds come work for us.
The only thing besides all their tech that I know is like every time you read a profile of any trading company, they're like, and they love to play backgammon there. They love to play like all the article the chess boards are out and they could be seen playing chess over lunch, et cetera. I get it. Okay, they like odds, they like games, they like whatever.
What is Hudson River Trading and how does it make money as a market‑making firm?
Let's move the ball forward.
Well, there's also the underlying theme of is this all hype? Yeah, yeah, right. Because you do get the sense sometimes that companies are putting out press releases where they just mention AI to tick a box to be seen to be doing something and hope that their stock actually goes up. And because so much of this is proprietary and people kind of have an excuse not to go into detail about it, sometimes you do get the feeling that people are just talking about it and not actually using it.
Cynics and I'm not saying this myself. I know you're not a cynics. Speaking of trading and technology, Cynics Would say that CME's deal with Google to build a cloud s to you put trading on the cloud was hyped.
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Chapters
4 chapters
1
What is the main topic discussed in this episode?
0:01–3:12
2
What is Hudson River Trading and how does it make money as a market‑making firm?
3:12–19:01
3
How does HRT’s AI approach differ from traditional hand‑crafted quant models?
19:01–40:10
4
What kinds of data does HRT use for its AI models and why is market‑data so valuable?
40:10–57:54
Speakers
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